Admission Open

Robotics Course in Mianwali

Robotics Course Outline
I. Introduction to Robotics

Definition and scope of robotics
History and evolution of robotics
Applications of robotics in various fields (industry, healthcare, service, exploration)
Fundamentals of Robotics

Components of a robot (sensors, actuators, controllers)
Types of robots (manipulators, mobile robots, humanoid robots, aerial robots)
Basic concepts of robot kinematics and dynamics
II. Robot Kinematics
Forward Kinematics

Joint coordinates and configuration space
Homogeneous transformation matrices
Deriving forward kinematic equations
Inverse Kinematics

The inverse kinematics problem
Analytical and numerical solutions
Applications in robotic arm positioning
Velocity Kinematics

Linear and angular velocity
Jacobian matrix and its applications
Singularities and manipulability
III. Robot Dynamics
Rigid Body Dynamics

Newton-Euler formulation
Lagrangian mechanics
Equations of motion for robotic systems
Dynamic Simulation

Simulating robot motion
Using simulation tools (e.g., MATLAB, Gazebo)
Dynamic control strategies
IV. Robot Control
Control Theory Basics

Open-loop and closed-loop control
Proportional, Integral, Derivative (PID) control
Stability and performance analysis
Advanced Control Techniques

Model predictive control (MPC)
Adaptive control
Nonlinear control methods
Practical Control Systems

Implementing control algorithms
Real-time control and embedded systems
Case studies of control in industrial robots
V. Robot Perception
Sensors and Sensing

Types of sensors (proximity, vision, force, inertial)
Sensor integration and calibration
Signal processing for robotics
Computer Vision

Basics of image processing
Object detection and recognition
3D vision and depth sensing
SLAM (Simultaneous Localization and Mapping)

Overview of SLAM techniques
Laser-based and vision-based SLAM
Applications in autonomous navigation
VI. Robot Motion Planning
Path Planning

Graph-based methods (Dijkstra, A*)
Sampling-based methods (RRT, PRM)
Optimization-based planning
Trajectory Planning

Polynomial and spline trajectories
Time-optimal and energy-optimal trajectories
Real-time trajectory generation
Motion Planning for Mobile Robots

Navigation algorithms
Obstacle avoidance techniques
Multi-robot coordination
VII. Robot Learning and AI
Introduction to Robot Learning

Machine learning basics
Supervised, unsupervised, and reinforcement learning
Applications in robotics
Reinforcement Learning in Robotics

Markov decision processes (MDPs)
Q-learning, Deep Q-Networks (DQNs)
Policy gradient methods
Deep Learning for Robotics

Neural network architectures
Convolutional Neural Networks (CNNs) for vision
Applications of deep learning in perception and control
VIII. Human-Robot Interaction
Human-Robot Collaboration
Safety and ergonomics
Shared control and teleoperation

Admission Open for this course 
Contact Number: 03307615544

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